- Title
- Multiheaded deep learning chatbot for increasing production and marketing
- Creator
- Zheng, Shiyong; Yahya, Zahrah; Wang, Lei; Zhang, Ruihang; Hoshyar, Azadeh
- Date
- 2023
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/197620
- Identifier
- vital:18931
- Identifier
-
https://doi.org/10.1016/j.ipm.2023.103446
- Identifier
- ISSN:0306-4573 (ISSN)
- Abstract
- Some businesses on product development prefer to use a chatbot for judging the customer's view. Today, the ability of a chatbot to consider the context is challenging due to its technical nature. Sometimes, it may misjudge the context, making the wrong decision in predicting the product's originality in the market. This task of chatbot helps the enterprise make huge profits from accurate predictions. However, chatbots may commit errors in dialogs and bring inappropriate responses to users, reducing the confidentiality of product and marketing information. This, in turn, reduces the enterprise gain and imposes cost complications on businesses. To improve the performance of chatbots, AI models are used based on deep learning concepts. This research proposes a multi-headed deep neural network (MH-DNN) model for addressing the logical and fuzzy errors caused by retrieval chatbot models. This model cuts down on the error raised from the information loss. Our experiments extensively trained the model on a large Ubuntu dialog corpus. The recall evaluation scores showed that the MH-DNN approach slightly outperformed selected state-of-the-art retrieval-based chatbot approaches. The results obtained from the MHDNN augmentation approach were pretty impressive. In our proposed work, the MHDNN algorithm exhibited accuracy rates of 94% and 92%, respectively, with and without the help of the Seq2Seq technique. © 2023 Elsevier Ltd
- Publisher
- Elsevier Ltd
- Relation
- Information Processing and Management Vol. 60, no. 5 (2023), p.
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © 2023 Elsevier Ltd
- Subject
- 4609 Information systems; 4610 Library and information studies; Artificial intelligence; Business product development; Chatbot; Deep neural network; Marketing
- Reviewed
- Funder
- This work was sponsored by the following funds: "Research on Multidimensional Identification and Long-term Governance Mechanism of Relative Poverty of Farming Households in Border Areas" (Grant No. 20BGL247), General Project of the National Social Science Foundation of China; Research on Branding Strategies for High-Quality Agricultural Development in Ethnic Areas of Southwest China under the "Two Mountains" Theory, General Project of the National Social Science Foundation of China (Grant No. 21BGL129); China Postdoctoral Science Foundation
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